Trait generation

source('../env.R')

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Please cite the eBird Status and Trends data using: 
  Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, S. Ligocki, O. Robinson, 
  W. Hochachka, L. Jaromczyk, C. Crowley, K. Dunham, A. Stillman, I. Davies, 
  A. Rodewald, V. Ruiz-Gutierrez, C. Wood. 2023.
  eBird Status and Trends, Data Version: 2022; Released: 2023. Cornell Lab of
  Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2022

This version of the package provides access to the 2022 version of the eBird
Status and Trends Data Products. Access to the 2022 data will be provided 
until November 2024 when it will be replaced by the 2023 data. At that 
point, you will be required to update this R package and transition to using 
the new data.
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If you use the ggtree package suite in published research, please cite the appropriate paper(s):

Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam. ggtree: an R package for visualization and annotation
of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution. 2017, 8(1):28-36.
doi:10.1111/2041-210X.12628

Shuangbin Xu, Lin Li, Xiao Luo, Meijun Chen, Wenli Tang, Li Zhan, Zehan Dai, Tommy T. Lam, Yi Guan, Guangchuang Yu. Ggtree:
A serialized data object for visualization of a phylogenetic tree and annotation data. iMeta 2022, 1(4):e56.
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G Yu. Data Integration, Manipulation and Visualization of Phylogenetic Trees (1st ed.). Chapman and Hall/CRC. 2022. ISBN:
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Create measured traits database for Jetz Columbidae Taxonomy

Here we create traits based on the “AVONET: Morphological, Ecological and Geographical Data for All Birds.” (2022) morphology and trait database.

Load Mapping Birdlife to Jetz

birdlife_jetz_mapping = read_csv(MY_BIRDLIFE_COL_MAPPING)

Rows: 353 Columns: 35── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (24): SpeciesName_v8, CommonName_v8, RedList2023, Synonyms_v8, SpeciesName_v7, CommonName_v7, RedList2022, Synonyms_v7, SpeciesName...
dbl (11): SISRecID_v8, SpcRecID_v8, SISRecID_v7, SpcRecID_v7, SISRecID_v6, SpcRecID_v6, SISRecID_v5, SpcRecID_v5, SISRecID_v4, SpcRecID...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

birdlife_jetz_mapping

Load Avonet Traits

avonet_birdlife = read_csv(DL_AVONET)

Rows: 11009 Columns: 37── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): Species1, Family1, Order1, Avibase.ID1, Mass.Source, Mass.Refs.Other, Inference, Traits.inferred, Reference.species, Habitat,...
dbl (24): Sequence, Total.individuals, Female, Male, Unknown, Complete.measures, Beak.Length_Culmen, Beak.Length_Nares, Beak.Width, Bea...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

avonet_birdlife

Avonet Columbidae traits

avonet_columbidae = avonet_birdlife[avonet_birdlife$Order1 == 'Columbiformes',]
avonet_columbidae

Check we have all species from our mapping file (e.g. all species mapping to Birdlife V8 taxonomy)

Avonet uses Birdlife V5 taxonomy.

birdlife_jetz_mapping[!(birdlife_jetz_mapping$SpeciesName_v5 %in% avonet_columbidae$Species1),]

Check which species we are losing from avonet

We can check that we are only losing species that are absent from Birdlife V8 by checking our Birdlife to Jetz mapping scripts.

avonet_columbidae[!(avonet_columbidae$Species1 %in% birdlife_jetz_mapping$SpeciesName_v5),]

Map traits to Jetz

avonet_jetz = avonet_columbidae %>%
  inner_join(birdlife_jetz_mapping[,c('SpeciesName_v5', 'JetzSpeciesName')], by=c('Species1' = 'SpeciesName_v5')) %>%
  group_by(JetzSpeciesName) %>%
  summarise(
    beak_length_culmen = mean(Beak.Length_Culmen),
    beak_length_nares = mean(Beak.Length_Nares),
    beak_width = mean(Beak.Width),
    beak_depth = mean(Beak.Depth),
    tarsus_length = mean(Tarsus.Length),
    wing_length = mean(Wing.Length),
    kipps_distance = mean(Kipps.Distance),
    secondary1 = mean(Secondary1),
    handwing_index = mean(`Hand-Wing.Index`),
    tail_length = mean(Tail.Length),
    mass = mean(Mass),
    habitat = toString(unique(na.omit(Habitat))),
    habitat_density = toString(unique(na.omit(Habitat.Density))),
    migration =  toString(unique(na.omit(Migration))),
    trophic_level =  toString(unique(na.omit(Trophic.Level))),
    trophic_niche =  toString(unique(na.omit(Trophic.Niche))),
    primary_lifestyle =  toString(unique(na.omit(Primary.Lifestyle)))
  )

avonet_jetz

Check we have just one row per species

length(unique(avonet_jetz$JetzSpeciesName)) == nrow(avonet_jetz)

[1] TRUE

Check for mapping species that have two factor values

avonet_jetz[
  grepl(',', avonet_jetz$habitat, fixed = TRUE) |
  grepl(',', avonet_jetz$habitat_density, fixed = TRUE) |
  grepl(',', avonet_jetz$migration, fixed = TRUE) |
  grepl(',', avonet_jetz$trophic_level, fixed = TRUE) |
  grepl(',', avonet_jetz$trophic_niche, fixed = TRUE) |
  grepl(',', avonet_jetz$primary_lifestyle, fixed = TRUE),
  c('JetzSpeciesName', 'habitat', 'habitat_density', 'migration', 'trophic_level', 'trophic_niche', 'primary_lifestyle')
]

Species that are migratory and year round residents

avonet_jetz[avonet_jetz$JetzSpeciesName %in% c('Columba_palumbus', 'Patagioenas_fasciata'),]

avonet_jetz$migration[avonet_jetz$JetzSpeciesName %in% c('Macropygia_amboinensis', 'Treron_calvus')] = '1'

Tropic niche of Macropygia amboinensis from avibase is Omnivore https://avibase.bsc-eoc.org/species.jsp?lang=EN&avibaseid=FD5160925CD583C1&sec=lifehistory

avonet_jetz$trophic_niche[avonet_jetz$JetzSpeciesName %in% c('Macropygia_amboinensis')] = 'Omnivore'

Should now have no species with dual factor values

avonet_jetz[
  grepl(',', avonet_jetz$habitat, fixed = TRUE) |
  grepl(',', avonet_jetz$habitat_density, fixed = TRUE) |
  grepl(',', avonet_jetz$migration, fixed = TRUE) |
  grepl(',', avonet_jetz$trophic_level, fixed = TRUE) |
  grepl(',', avonet_jetz$trophic_niche, fixed = TRUE) |
  grepl(',', avonet_jetz$primary_lifestyle, fixed = TRUE),
  c('JetzSpeciesName', 'habitat', 'habitat_density', 'migration', 'trophic_level', 'trophic_niche', 'primary_lifestyle')
]

avonet_jetz$habitat = as.factor(avonet_jetz$habitat)
avonet_jetz$habitat_density = as.factor(avonet_jetz$habitat_density)
avonet_jetz$migration = as.factor(avonet_jetz$migration)
avonet_jetz$trophic_level = as.factor(avonet_jetz$trophic_level)
avonet_jetz$trophic_niche = as.factor(avonet_jetz$trophic_niche)
avonet_jetz$primary_lifestyle = as.factor(avonet_jetz$primary_lifestyle)

Generate composite traits

This uses a methodogy from Trisos (2014) and Tom P. Bregman and Tobias (2016) to create traits that describe a species lifestyle.

These traits are namely: * Overall body size. * trophic - beak shape (beak length, width, depth) - measures competitive exclusion (e.g. over-dispersion) via accentuation of foraging differences * locomotory (wing, tail, tarsus length) * tarsus-to-tail length ratio

Trophic trait

set.seed(778899)
trophic_trait_pca = rda(avonet_jetz[,c('beak_length_culmen', 'beak_width', 'beak_depth')])

avonet_jetz$trophic_pc1 = trophic_trait_pca$Ybar[,1]
avonet_jetz$trophic_pc2 = trophic_trait_pca$Ybar[,2]
avonet_jetz$trophic_pc3 = trophic_trait_pca$Ybar[,3]

How does PC1 explain trophic niche?

trophic_pc1_aov = aov(trophic_pc1 ~ trophic_niche,
  data = avonet_jetz
)

summary(trophic_pc1_aov)

               Df Sum Sq Mean Sq F value        Pr(>F)    
trophic_niche   2  3.676  1.8382   20.87 0.00000000326 ***
Residuals     301 26.514  0.0881                          
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(trophic_pc1_aov,
  linfct = mcp(trophic_niche = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = trophic_pc1 ~ trophic_niche, data = avonet_jetz)

Linear Hypotheses:
                            Estimate Std. Error t value   Pr(>|t|)    
Granivore - Frugivore == 0 -0.247299   0.040557  -6.098 < 0.000001 ***
Omnivore - Frugivore == 0   0.008417   0.044323   0.190       0.98    
Omnivore - Granivore == 0   0.255716   0.050218   5.092 0.00000197 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

How does PC2 explain trophic niche?

trophic_pc2_aov = aov(trophic_pc2 ~ trophic_niche,
  data = avonet_jetz
)

summary(trophic_pc2_aov)

               Df Sum Sq Mean Sq F value              Pr(>F)    
trophic_niche   2 0.7255  0.3627   63.96 <0.0000000000000002 ***
Residuals     301 1.7070  0.0057                                
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(trophic_pc2_aov,
  linfct = mcp(trophic_niche = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = trophic_pc2 ~ trophic_niche, data = avonet_jetz)

Linear Hypotheses:
                           Estimate Std. Error t value Pr(>|t|)    
Granivore - Frugivore == 0 -0.11184    0.01029 -10.868 < 0.0001 ***
Omnivore - Frugivore == 0  -0.07054    0.01125  -6.272 < 0.0001 ***
Omnivore - Granivore == 0   0.04131    0.01274   3.242  0.00361 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

How does PC3 explain trophic niche?

trophic_pc3_aov = aov(trophic_pc3 ~ trophic_niche,
  data = avonet_jetz
)

summary(trophic_pc3_aov)

               Df Sum Sq Mean Sq F value               Pr(>F)    
trophic_niche   2 0.5426  0.2713   40.48 0.000000000000000269 ***
Residuals     301 2.0171  0.0067                                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(trophic_pc3_aov,
  linfct = mcp(trophic_niche = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = trophic_pc3 ~ trophic_niche, data = avonet_jetz)

Linear Hypotheses:
                           Estimate Std. Error t value Pr(>|t|)    
Granivore - Frugivore == 0 -0.10063    0.01119  -8.995 < 0.0001 ***
Omnivore - Frugivore == 0  -0.03585    0.01223  -2.933  0.00977 ** 
Omnivore - Granivore == 0   0.06477    0.01385   4.676 < 0.0001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

avonet_jetz %>% 
  pivot_longer(cols = c('trophic_pc1', 'trophic_pc2', 'trophic_pc3'), names_to = 'axis') %>% 
  ggplot(aes(x = trophic_niche, y = value, color = axis)) + 
    geom_boxplot() + xlab('Trophic Niche') + ylab('PC Value')

avonet_jetz %>% 
  ggplot(aes(x = trophic_niche, y = trophic_pc1, fill = trophic_niche)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Trophic PC1")

avonet_jetz %>% 
  ggplot(aes(x = trophic_niche, y = trophic_pc2, fill = trophic_niche)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Trophic PC2")

ggarrange(ncol = 2, nrow = 2, common.legend = T,
          ggplot(avonet_jetz, aes(y = trophic_pc1, x = trophic_pc2, color = trophic_niche)) + geom_point(),
          ggplot(avonet_jetz, aes(y = trophic_pc1, x = trophic_pc3, color = trophic_niche)) + geom_point(),
          ggplot(avonet_jetz, aes(y = trophic_pc3, x = trophic_pc2, color = trophic_niche)) + geom_point()
)

Is PC1 correlated to mass?

cor(avonet_jetz$trophic_pc1, avonet_jetz$mass)

[1] 0.7997893

Is PC2 correlated to mass?

cor(avonet_jetz$trophic_pc2, avonet_jetz$mass)

[1] 0.6423876

Is PC3 correlated to mass?

cor(avonet_jetz$trophic_pc3, avonet_jetz$mass)

[1] 0.5944833

Locomotory trait

set.seed(778899)
loco_trait_pca = rda(avonet_jetz[,c('tarsus_length', 'wing_length', 'tail_length')])
summary(loco_trait_pca)

Call:
rda(X = avonet_jetz[, c("tarsus_length", "wing_length", "tail_length")]) 

Partitioning of variance:
              Inertia Proportion
Total            4004          1
Unconstrained    4004          1

Eigenvalues, and their contribution to the variance 

Importance of components:
                           PC1       PC2      PC3
Eigenvalue            3599.459 350.94577 53.31631
Proportion Explained     0.899   0.08765  0.01332
Cumulative Proportion    0.899   0.98668  1.00000

Scaling 2 for species and site scores
* Species are scaled proportional to eigenvalues
* Sites are unscaled: weighted dispersion equal on all dimensions
* General scaling constant of scores:  33.18766 


Species scores

                 PC1     PC2     PC3
tarsus_length  3.026  0.8724  3.7968
wing_length   25.061  5.8029 -0.4984
tail_length   18.788 -7.8810  0.0533


Site scores (weighted sums of species scores)

            PC1          PC2       PC3
sit1   -0.51315   2.55439912 -1.473133
sit2   -0.77734   1.97068701 -0.285657
sit3   -0.45789   1.23403715 -0.423677
sit4   -0.99167  -0.01969641  0.474162
sit5    2.10770   6.62482101  1.139124
sit6   -1.01073   0.87860796  0.243452
sit7   -1.33630   0.23657428  0.373900
sit8   -1.84945  -0.65747797  0.277173
sit9   -1.88647  -0.68187484  0.338621
sit10  -2.15755  -0.67734893 -1.000295
sit11   0.37445   0.80128589 -1.329999
sit12   1.22660   3.59349812 -1.412819
sit13   2.37934   1.01099884 -1.606495
sit14   1.83448   1.78047900 -1.586082
sit15   1.88153  -0.29524719 -0.514557
sit16   0.10264   0.27820916 -1.625294
sit17   1.75537  -0.91833428 -1.271681
sit18   0.59958   3.36723304 -1.466872
sit19   1.64567   3.09396968 -1.605706
sit20   1.93744   1.40119053 -2.028735
sit21  -0.70539   0.73906399 -1.376901
sit22   3.11613  -1.54048403 -0.685654
sit23   2.09449  -0.31769092 -0.099696
sit24   1.94751   1.98397358 -1.959963
sit25   1.67820   3.52720836 -1.546812
sit26   1.18508   3.55432927 -1.950158
sit27  -0.06267   0.92322458 -1.826103
sit28   1.32702   2.13025288 -1.762425
sit29   0.61603   2.84816963 -1.466079
sit30   1.87654   2.73042507 -1.653498
sit31   2.52253   1.60674405 -1.515105
sit32   2.78368   1.31654397 -1.393184
sit33   2.27242  -0.30102206 -2.194325
sit34   1.01364   1.66278492 -1.694973
sit35   1.87513   0.64421323 -1.936839
sit36   1.41142   3.14347149 -1.772778
sit37   1.36485   2.26775478 -2.271911
sit38   2.41145   0.61546720 -1.988945
sit39   0.93290  -0.02443220 -1.321982
sit40   2.70292  -0.16216134 -0.198937
sit41   1.30103   1.63813645 -1.575965
sit42   1.72776   0.72977557 -1.127928
sit43  -3.00722  -1.41561609 -0.153493
sit44  -3.04393  -0.94463692 -0.127102
sit45  -3.48638  -2.30211568  0.142357
sit46  -2.38409  -2.81305196 -0.539749
sit47  -3.49043  -0.84159324 -0.110975
sit48  -3.21300  -1.09545572 -0.352763
sit49  -2.81968  -1.76070075 -0.961126
sit50  -2.28195  -2.82333945 -0.539774
sit51  -2.96009  -1.27817576 -0.196296
sit52   3.15438  -2.75027904  0.193928
sit53   0.30875   2.25502408  2.259988
sit54  -1.12545   1.39948588  0.439076
sit55   2.28999   2.25953790 -1.260281
sit56   2.32253   1.19618070 -1.103348
sit57   2.55669  -0.55310346 -1.770365
sit58   1.51521  -0.11297160 -0.159131
sit59   1.29361   1.35542473 -0.425252
sit60   1.79388   2.11695083 -1.314984
sit61   1.24413   2.80111494 -0.928681
sit62   1.19894   1.72384069 -1.698657
sit63   1.75166  -0.06836513  0.010896
sit64   1.91299  -0.32421419 -0.875359
sit65   3.92357   1.57311346 -1.280767
sit66   1.01296   1.21059903 -0.774069
sit67   3.16828  -1.31438773 -0.647479
sit68   4.90780   0.55745069  0.670252
sit69   4.82333   0.62542854 -0.779292
sit70   1.71255  -0.63765328 -0.959651
sit71   2.48776  -0.03109035 -0.663343
sit72   2.77132   0.69155469 -1.266068
sit73   2.79482   0.73843013 -0.742476
sit74   2.58497  -0.61335800 -0.949426
sit75   2.15871   1.73197985 -1.095895
sit76   2.42934   2.83430693 -1.038096
sit77   1.83151   0.06845541 -0.400232
sit78   2.88177   0.67190471  0.046313
sit79   2.99619   1.58057759 -0.675060
sit80   2.39995   0.53175486 -0.933379
sit81   2.90208   3.07284069 -1.074608
sit82   2.47048   1.56199036 -1.041936
sit83   1.84395  -0.48708749 -0.624893
sit84   1.39207   0.05462843 -1.390043
sit85   2.12834   1.24657407 -0.249044
sit86   2.13253   2.89135336 -0.551274
sit87   0.68313   1.92686129  0.009014
sit88   2.43192   1.96449769 -1.103681
sit89   1.82208   3.55821148 -0.773121
sit90   3.24186   0.20698967 -0.502579
sit91   1.63006   1.79568200 -0.452161
sit92  -2.67292   0.65752929  1.869202
sit93  -2.30581   0.65616930  2.656772
sit94  -0.69952   0.14730133  2.863059
sit95  -1.40845   1.45826721  1.941658
sit96  -0.66853  -0.44139989  0.576893
sit97  -1.09199   0.61780386  0.644687
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sit99  -1.12070   1.16751829  1.989836
sit100 -1.21340   1.01088978  2.126043
sit101 -0.48835   0.60813225  2.193811
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sit192 -0.71074  -1.49429291 -0.762925
sit193 -1.07747  -0.65132273 -0.760903
sit194 -1.28331  -0.28706863 -0.048439
sit195 -1.70323  -0.27249173 -0.595042
sit196  1.03085   0.59590396 -0.410057
sit197 -0.37790   0.66918129  0.432488
sit198  0.15229   4.10506356 -1.121890
sit199 -2.84409   0.49562513 -0.195880
sit200 -1.99175   1.66685655  0.119917
sit201 -0.11558  -4.19294320 -0.450413
sit202 -1.31657   0.02040541  0.855583
sit203  0.31619  -0.74145966 -0.652559
sit204 -1.63922  -0.11238852  1.143241
sit205 -2.22362   0.73008407 -0.375533
sit206  0.37947   0.06338301  0.363126
sit207 -1.27511   1.34350627 -0.113567
sit208 -2.18564   0.31796124  0.347331
sit209  0.47149  -1.87341964  0.497752
sit210 -2.02943   0.63934119  0.307014
sit211 -2.21902   0.36289964 -0.005121
sit212  0.02184  -1.19471987  0.436800
sit213 -2.01694   1.37684650 -0.109422
sit214 -1.93269   1.42828942  0.312024
sit215 -0.85358  -0.45745834  1.518196
sit216 -2.42525   1.77490543  0.045519
sit217 -1.22014  -0.00623636 -1.427514
sit218 -2.55125   1.83219151  0.396152
sit219 -1.02230  -0.28887175 -0.387737
sit220 -2.27094   0.97581833  0.106136
sit221  0.36377  -2.44147759 -0.497410
sit222  0.73655  -1.65415040 -0.474994
sit223 -1.97741  -0.11448090 -0.035324
sit224  0.03243  -0.76096313  0.802778
sit225 -2.89286   0.00009782  0.009514
sit226 -3.38601   0.64900998 -0.378650
sit227 -0.44666   0.02054746  0.056893
sit228 -1.24302   1.98821250 -0.524307
sit229 -1.89221   1.22035457  0.327541
sit230 -0.80572   1.52658387  0.339419
sit231 -1.72323   0.60635696  0.070563
sit232 -1.80777   1.01419310  0.116662
sit233 -0.94583   0.04652115 -0.262361
sit234 -2.60327   0.38146929 -0.651302
sit235 -1.32968   1.07703099  0.359882
sit236 -1.70870  -0.08913622  0.929897
sit237 -1.90955   1.04608937 -0.566758
sit238 -1.81381   0.71728132 -0.383155
sit239 -1.81198   0.51996729 -0.181245
sit240 -1.90789   0.39961480  0.818320
sit241 -2.46323   1.02915203 -0.213778
sit242  0.12569  -1.38917274  0.117773
sit243 -1.66488   0.99619132 -0.381567
sit244 -1.17338   1.66358772  0.100546
sit245 -2.30985   1.11163620  0.132278
sit246 -2.47643   1.04346284 -0.009008
sit247 -1.26831   1.96581558 -0.551684
sit248  3.14518  -3.26405079 -1.234615
sit249  2.19063  -3.48551554 -1.450136
sit250  5.24569 -10.78583410 -2.049622
sit251 -0.98263  -0.37869901  2.366742
sit252 -0.48791  -3.17780974 -0.531762
sit253 -0.99325  -2.01243851 -0.936698
sit254 -0.16778  -0.76944017 -0.234072
sit255 -0.52887  -0.73870287 -0.677634
sit256  0.53749  -1.28384897 -1.257399
sit257  0.28737  -1.47145584 -0.870096
sit258  0.14787   0.03070789 -1.630811
sit259  0.16874  -0.10976982 -1.615166
sit260  0.50292  -0.81663207 -0.665590
sit261 -0.83681  -1.29583213 -0.778287
sit262 -0.35030  -0.16473613 -1.267878
sit263  0.27795  -0.46764660 -1.093222
sit264 -1.43005   0.26629625 -1.586214
sit265  0.01431   0.23438047 -1.360462
sit266 -1.10117  -0.80949604 -0.784684
sit267  1.02695  -5.28889805 -0.503417
sit268 -0.73171   0.96920300 -1.224442
sit269 -0.36209   1.35849595 -0.778829
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sit271 -1.55303   1.04341080 -0.929296
sit272 -0.98440   0.69008148 -0.326811
sit273  1.02566   0.21278024 -0.818303
sit274 -1.26889   1.07065501 -1.372712
sit275 -1.05745   0.59209646 -0.325097
sit276 -2.17462   0.76580188 -1.107567
sit277 -0.14598  -1.39838448 -0.541514
sit278 -0.54267   2.42770948 -0.419667
sit279  0.40127   1.58906815 -1.242661
sit280 -0.88415   1.58940503 -0.565059
sit281 -0.31812   0.90753291 -0.407378
sit282 -0.26631   1.50109745 -0.321668
sit283  0.18600  -3.42732087 -1.051037
sit284  0.47603   0.28113418 -1.475808
sit285  0.15282  -0.36152398 -1.333934
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sit299 -1.18158  -0.47960233 -0.585695
sit300 -0.78541   0.02120601 -0.516088
sit301 -1.98037   1.17408954  0.171393
sit302 -0.10398  -3.11320182  1.267585
sit303 -0.42240  -2.39117001 -1.120275
sit304 -0.46773   0.22195373 -0.262311

avonet_jetz$locomotory_pc1 = loco_trait_pca$Ybar[,1]
avonet_jetz$locomotory_pc2 = loco_trait_pca$Ybar[,2]
avonet_jetz$locomotory_pc3 = loco_trait_pca$Ybar[,3]

How does PC1 explain lifstyle?

loco_lifstyle_pc1_aov = aov(locomotory_pc1 ~ primary_lifestyle,
  data = avonet_jetz
)

summary(loco_lifstyle_pc1_aov)

                   Df Sum Sq Mean Sq F value Pr(>F)  
primary_lifestyle   2   1.77  0.8871    3.08 0.0474 *
Residuals         301  86.68  0.2880                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(loco_lifstyle_pc1_aov,
  linfct = mcp(primary_lifestyle = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = locomotory_pc1 ~ primary_lifestyle, data = avonet_jetz)

Linear Hypotheses:
                                Estimate Std. Error t value Pr(>|t|)  
Insessorial - Generalist == 0  -0.004717   0.154295  -0.031   0.9995  
Terrestrial - Generalist == 0   0.152607   0.156886   0.973   0.5782  
Terrestrial - Insessorial == 0  0.157324   0.064159   2.452   0.0351 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

How does PC2 explain lifstyle?

loco_lifstyle_pc2_aov = aov(locomotory_pc2 ~ primary_lifestyle,
  data = avonet_jetz
)

summary(loco_lifstyle_pc2_aov)

                   Df Sum Sq Mean Sq F value     Pr(>F)    
primary_lifestyle   2  195.8   97.89   13.33 0.00000284 ***
Residuals         301 2210.6    7.34                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(loco_lifstyle_pc2_aov,
  linfct = mcp(primary_lifestyle = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = locomotory_pc2 ~ primary_lifestyle, data = avonet_jetz)

Linear Hypotheses:
                               Estimate Std. Error t value Pr(>|t|)    
Insessorial - Generalist == 0    1.2096     0.7792   1.552    0.252    
Terrestrial - Generalist == 0   -0.4475     0.7923  -0.565    0.830    
Terrestrial - Insessorial == 0  -1.6571     0.3240  -5.115  <0.0001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

How does PC3 explain lifstyle?

loco_lifstyle_pc3_aov = aov(locomotory_pc3 ~ primary_lifestyle,
  data = avonet_jetz
)

summary(loco_lifstyle_pc3_aov)

                   Df Sum Sq Mean Sq F value   Pr(>F)    
primary_lifestyle   2   71.1   35.57   7.447 0.000697 ***
Residuals         301 1437.7    4.78                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(glht(loco_lifstyle_pc3_aov,
  linfct = mcp(primary_lifestyle = "Tukey")
))

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: aov(formula = locomotory_pc3 ~ primary_lifestyle, data = avonet_jetz)

Linear Hypotheses:
                               Estimate Std. Error t value Pr(>|t|)    
Insessorial - Generalist == 0    0.3500     0.6284   0.557 0.834768    
Terrestrial - Generalist == 0   -0.6582     0.6389  -1.030 0.541379    
Terrestrial - Insessorial == 0  -1.0082     0.2613  -3.858 0.000362 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)

avonet_jetz %>% 
  pivot_longer(cols = c('locomotory_pc1', 'locomotory_pc2', 'locomotory_pc3'), names_to = 'axis') %>% 
  ggplot(aes(x = primary_lifestyle, y = value, color = axis)) + 
    geom_boxplot() + xlab('Primary Lifestype') + ylab('PC Value')

avonet_jetz %>% 
  ggplot(aes(x = primary_lifestyle, y = locomotory_pc1, fill = primary_lifestyle)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Locomotory PC1")

avonet_jetz %>% 
  ggplot(aes(x = primary_lifestyle, y = locomotory_pc2, fill = primary_lifestyle)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Locomotory PC2")

avonet_jetz %>% 
  pivot_longer(cols = c('locomotory_pc1', 'locomotory_pc2', 'locomotory_pc3'), names_to = 'axis') %>% 
  ggplot(aes(x = migration, y = value, color = axis)) + 
    geom_boxplot() + xlab('Migration') + ylab('PC Value')

avonet_jetz %>% 
  ggplot(aes(x = migration, y = locomotory_pc1, fill = migration)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Locomotory PC1")

avonet_jetz %>% 
  ggplot(aes(x = migration, y = locomotory_pc2, fill = migration)) +
    geom_violin(alpha = 0.5) +
    geom_point(position = position_jitter(seed = 1, width = 0.2)) +
    theme(legend.position = "none") +
    labs(title = "Locomotory PC2")

ggarrange(ncol = 2, nrow = 2, common.legend = T,
          ggplot(avonet_jetz, aes(y = locomotory_pc1, x = locomotory_pc2, color = primary_lifestyle)) + geom_point(),
          ggplot(avonet_jetz, aes(y = locomotory_pc1, x = locomotory_pc3, color = primary_lifestyle)) + geom_point(),
          ggplot(avonet_jetz, aes(y = locomotory_pc3, x = locomotory_pc2, color = primary_lifestyle)) + geom_point()
)

Is PC1 correlated to mass?

cor(avonet_jetz$locomotory_pc1, avonet_jetz$mass)

[1] 0.8332333

Is PC2 correlated to mass?

cor(avonet_jetz$locomotory_pc2, avonet_jetz$mass)

[1] 0.7988238

Is PC3 correlated to mass?

cor(avonet_jetz$locomotory_pc3, avonet_jetz$mass)

[1] 0.6700626

Store our new trait data

Here we select the second axis of each trait PCA.

trait_database = avonet_jetz %>%
  dplyr::select(JetzSpeciesName, beak_width, trophic_pc2, locomotory_pc2, mass, handwing_index) %>%
  rename(jetz_species_name=JetzSpeciesName, gape_width=beak_width, trophic_trait=trophic_pc2, locomotory_trait=locomotory_pc2)
trait_database

write_csv(trait_database, filename(TAXONOMY_OUTPUT_DIR, 'traits_jetz.csv'))

Check which single traits are associated with mass

ggplot(trait_database, aes(x = mass, y = gape_width)) + geom_point()

summary(lm(gape_width ~ mass, data = trait_database))

Call:
lm(formula = gape_width ~ mass, data = trait_database)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1630 -0.8190 -0.2109  0.6962  4.9974 

Coefficients:
             Estimate Std. Error t value            Pr(>|t|)    
(Intercept) 3.5774864  0.0979691   36.52 <0.0000000000000002 ***
mass        0.0040627  0.0002789   14.57 <0.0000000000000002 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.197 on 302 degrees of freedom
Multiple R-squared:  0.4127,    Adjusted R-squared:  0.4107 
F-statistic: 212.2 on 1 and 302 DF,  p-value: < 0.00000000000000022

ggplot(trait_database, aes(x = mass, y = handwing_index)) + geom_point()

summary(lm(handwing_index ~ mass, data = trait_database))

Call:
lm(formula = handwing_index ~ mass, data = trait_database)

Residuals:
     Min       1Q   Median       3Q      Max 
-15.8365  -3.6549  -0.1984   4.0155  17.4322 

Coefficients:
             Estimate Std. Error t value            Pr(>|t|)    
(Intercept) 31.583521   0.465849  67.798 <0.0000000000000002 ***
mass        -0.001694   0.001326  -1.277               0.202    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.693 on 302 degrees of freedom
Multiple R-squared:  0.005373,  Adjusted R-squared:  0.00208 
F-statistic: 1.631 on 1 and 302 DF,  p-value: 0.2025

“AVONET: Morphological, Ecological and Geographical Data for All Birds.” 2022. Ecology Letters. https://doi.org/10.1111/ele.13898.

Tom P. Bregman, Hannah E. A. MacGregor, Alexander C. Lees, and Joseph A. Tobias. 2016. “Using Avian Functional Traits to Assess the Impact of Land-Cover Change on Ecosystem Processes Linked to Resilience in Tropical Forests” 283 (1844). https://doi.org/10.1098/rspb.2016.1289.

Trisos, Petchey, C. H. 2014. “Unraveling the Interplay of Community Assembly Processes Acting on Multiple Niche Axes Across Spatial Scales” 184 (5). https://doi.org/10.1086/678233.